An entity may be tracked, i.e. its position at a certain point in time may be estimated or determined, based on an entity's measurement of a given signal metric at locations that the entity visits.
In open outdoor environments, tracking is typically performed by measuring timing or carrier phase measurements. A position of the entity may then be estimated using simple propagation models and databases of transmitter sources.
However, in complex environments, such as indoors or other cluttered regions, these simple propagation calculations tend to fail. Conventional radio positioning methods tend to rely on a radio signal environment being smooth and easily modelled with simple mathematics. In cluttered and indoor regions, signal variations tend to be too complex for traditional radio positioning methods to function satisfactorily.
In a separate field to radio positioning methods, Simultaneous Localization and Mapping (SLAM) is a process by which one or more entities (typically robots or autonomous vehicles) can build a map of an unknown area and, at the same time, determine their current location within that map.
Distributed Particle Simultaneous Localisation and Mapping (DP-SLAM), such as that described in “DP-SLAM: Fast, robust simultaneous localization and mapping without predetermined landmarks”, A. Eliazar, R. Parr, International Joint Conference on Artificial Intelligence 2003, provides simultaneous localization and mapping without landmarks.